Neuro-response stimulus and stimulus attribute resonance estimator

ABSTRACT

An example system includes an analyzer to identify first activity in first neuro-response data, the first activity generated in response to exposure of a subject to a first stimulus prior to exposure to an advertisement or entertainment; identify second activity in second neuro-response data, the second activity generated in response to re-exposure of the subject to the first stimulus after to exposure to the advertisement or entertainment; calculate a differential event related potential measurement; and calculate a differential event related power spectral perturbation. The example system includes a resonance estimator to determine a subject resonance measurement to the advertisement or the entertainment based on the differential event related potential measurement and adjust at least one of the subject resonance measurement or the differential event related potential measurement based on the differential event related power spectral perturbation to generate an adjusted subject resonance measurement.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 16/944,714 (now U.S. Pat. No. 11,244,345), which was filed on Jul.31, 2020. U.S. patent application Ser. No. 16/944,714 is a continuationof U.S. patent application Ser. No. 16/183,131 (now U.S. Pat. No.10,733,625), which was filed on Nov. 7, 2018. U.S. patent applicationSer. No. 16/183,131 is a continuation of U.S. patent application Ser.No. 13/965,805, which was filed on Aug. 13, 2013. U.S. patentapplication Ser. No. 13/965,805 is a continuation of U.S. patentapplication Ser. No. 12/182,874 (now U.S. Pat. No. 8,533,042), which wasfiled on Jul. 30, 2008, and claims the benefit under 35 U.S.C. § 119(e)to U.S. Provisional Patent Application No. 60/952,723, which was filedon Jul. 30, 2007. This patent claims the benefit of U.S. patentapplication Ser. No. 16/944,714, U.S. patent application Ser. No.16/183,131, U.S. patent application Ser. No. 13/965,805, U.S. patentapplication Ser. No. 12/182,874, and U.S. Provisional Patent ApplicationNo. 60/952,723. U.S. patent application Ser. No. 16/944,714, U.S. patentapplication Ser. No. 16/183,131, U.S. patent application Ser. No.13/965,805, U.S. patent application Ser. No. 12/182,874, and U.S.Provisional Patent Application No. 60/952,723 are hereby incorporatedherein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to a stimulus and stimulus attributeresonance estimator.

BACKGROUND

Conventional systems for estimating stimulus and stimulus attributeresonance are limited. Some audience resonance measurement systems arebased on demographic information, statistical data, and survey basedresponse collection. However, conventional systems are subject tosemantic, syntactic, metaphorical, cultural, and interpretive errors.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular example embodiments.

FIG. 1 illustrates one example of a system for estimating stimulus andstimulus attribute resonance.

FIG. 2 illustrates examples of stimulus attributes that can be includedin a stimulus attributes repository.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with theresonance estimation system.

FIG. 5 illustrates one example of a report generated using the resonanceestimation system.

FIG. 6 illustrates one example of a technique for performing dataanalysis.

FIG. 7 illustrates one example of technique for estimating stimulus andstimulus attribute resonance.

FIG. 8 provides one example of a system that can be used to implementone or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of thedisclosure including the best modes contemplated by the inventors forcarrying out the disclosure. Examples of these specific embodiments areillustrated in the accompanying drawings. While the disclosure isdescribed in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the disclosure to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the disclosure as defined by the appendedclaims.

For example, the techniques and mechanisms of the present disclosurewill be described in the context of particular types of data such ascentral nervous system, autonomic nervous system, and effector data.However, it should be noted that the techniques and mechanisms of thepresent disclosure apply to a variety of different types of data. Itshould be noted that various mechanisms and techniques can be applied toany type of stimuli. In the following description, numerous specificdetails are set forth in order to provide a thorough understanding ofthe present disclosure. Particular example embodiments of the presentdisclosure may be implemented without some or all of these specificdetails. In other instances, well known process operations have not beendescribed in detail in order not to unnecessarily obscure the presentdisclosure.

Various techniques and mechanisms of the present disclosure willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present disclosureunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present disclosure will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

Disclosed herein are improved methods and apparatus for estimatingstimulus and stimulus attribute resonance.

A system determines neuro-response stimulus and stimulus attributeresonance. Stimulus material and stimulus material attributes such ascommunication, concept, experience, message, images, audio, pricing, andpackaging are evaluated using neuro-response data collected withmechanisms such as Event Related Potential (ERP), Electroencephalography(EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG),Electrooculography (EOG), eye tracking, and facial emotion encoding.Neuro-response data is analyzed to determine stimulus and stimulusattribute resonance.

EXAMPLES

Stimulus and stimulus attribute resonance estimators have been in usefor years. Typically, stimulus and stimulus attribute resonanceestimators are based on audience resonance measurement systems thattypically rely on demographic information, statistical information, andsurvey based response collection. One problem with conventional stimulusand stimulus attribute resonance estimators is that conventionalresonance estimators do not measure the inherent message resonance thatis attributable to the stimulus. They are also prone to semantic,syntactic, metaphorical, cultural, and interpretive errors therebypreventing the accurate and repeatable targeting of the audience.

Conventional systems do not use neuro-behavioral and neuro-physiologicalresponse blended manifestations in assessing the user response and donot elicit an individual customized neuro-physiological and/orneuro-behavioral response to the stimulus.

Conventional devices also fail to blend multiple datasets, and blendedmanifestations of multi-modal responses, across multiple datasets,individuals and modalities, to reveal and validate the elicited measuresof preference and resonance to stimulus and stimulus attributes

In these respects, the neuro-physiological and neuro-behavioral stimulusand stimulus attribute resonance estimator according to the presentdisclosure substantially departs from the conventional concepts anddesigns of the prior art, and in so doing provides an apparatusprimarily developed for the purpose of providing a method and a systemfor the neuro-physiological and neuro-behavioral response basedmeasurement of audience response and resonance to attributes ofmarketing, advertising and other audio/visual/tactile/olfactory stimulusincluding but not limited to communication, concept, experience,message, images, audio, pricing, packaging.

The techniques and mechanisms of the present disclosure useneuro-response measurements such as central nervous system, autonomicnervous system, and effector measurements to improve resonanceestimation. Some examples of central nervous system measurementmechanisms include Functional Magnetic Resonance Imaging (fMRI) andElectroencephalography (EEG). fMRI measures blood oxygenation in thebrain that correlates with increased neural activity. However, currentimplementations of fMRI have poor temporal resolution of few seconds.EEG measures electrical activity associated with post synaptic currentsoccurring in the milliseconds range. Subcranial EEG can measureelectrical activity with the most accuracy, as the bone and dermallayers weaken transmission of a wide range of frequencies. Nonetheless,surface EEG provides a wealth of electrophysiological information ifanalyzed properly. Even portable EEG with dry electrodes provide a largeamount of neuro-response information.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time etc.

According to various embodiments, the techniques and mechanisms of thepresent disclosure intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyperform resonance estimation. In some examples, autonomic nervous systemmeasures are themselves used to validate central nervous systemmeasures. Effector and behavior responses are blended and combined withother measures. According to various embodiments, central nervoussystem, autonomic nervous system, and effector system measurements areaggregated into a measurement that allows determination of stimulus andstimulus attribute resonance.

In particular embodiments, subjects are exposed to stimulus material anddata such as central nervous system, autonomic nervous system, andeffector data is collected during exposure. According to variousembodiments, data is collected in order to determine a resonance measurethat aggregates multiple component measures that assess resonance data.In particular embodiments, specific event related potential (ERP)analyses and/or event related power spectral perturbations (ERPSPs) areevaluated for different regions of the brain both before a subject isexposed to stimulus and each time after the subject is exposed tostimulus.

Pre-stimulus and post-stimulus differential as well as target anddistracter differential measurements of ERP time domain components atmultiple regions of the brain are determined (DERP). Event relatedtime-frequency analysis of the differential response to assess theattention, emotion and memory retention (DERPSPs) across multiplefrequency bands including but not limited to theta, alpha, beta, gammaand high gamma is performed. In particular embodiments, single trialand/or averaged DERP and/or DERPSPs can be used to enhance the resonancemeasure.

A resonance estimate may also incorporate relationship assessments usingbrain regional coherence measures of segments of the stimuli relevant tothe entity/relationship, segment effectiveness measures synthesizing theattention, emotional engagement and memory retention estimates based onthe neuro-physiological measures including time-frequency analysis ofEEG measurements, and differential saccade related neural signaturesduring segments where coupling/relationship patterns are emerging incomparison to segments with non-coupled interactions.

According to various embodiments, a resonance estimator can includeautomated systems with or without human intervention for the elicitationof potential object/individual groupings. For example, these could alsoinclude pattern recognition and object identification techniques. Thesesub-systems could include a hardware implementation and/or softwareimplementations.

A variety of stimulus materials such as entertainment and marketingmaterials, media streams, billboards, print advertisements, textstreams, music, performances, sensory experiences, etc. can be analyzed.According to various embodiments, enhanced neuro-response data isgenerated using a data analyzer that performs both intra-modalitymeasurement enhancements and cross-modality measurement enhancements.According to various embodiments, brain activity is measured not just todetermine the regions of activity, but to determine interactions andtypes of interactions between various regions. The techniques andmechanisms of the present disclosure recognize that interactions betweenneural regions support orchestrated and organized behavior. Attention,emotion, memory, and other abilities are not merely based on one part ofthe brain but instead rely on network interactions between brainregions.

The techniques and mechanisms of the present disclosure furtherrecognize that different frequency bands used for multi-regionalcommunication can be indicative of the effectiveness of stimuli. Inparticular embodiments, evaluations are calibrated to each subject andsynchronized across subjects. In particular embodiments, templates arecreated for subjects to create a baseline for measuring pre and poststimulus differentials. According to various embodiments, stimulusgenerators are intelligent and adaptively modify specific parameterssuch as exposure length and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillarydilation, EOG, eye tracking, facial emotion encoding, reaction time,etc. Individual modalities such as EEG are enhanced by intelligentlyrecognizing neural region communication pathways. Cross modalityanalysis is enhanced using a synthesis and analytical blending ofcentral nervous system, autonomic nervous system, and effectorsignatures. Synthesis and analysis by mechanisms such as time and phaseshifting, correlating, and validating intra-modal determinations allowgeneration of a composite output characterizing the significance ofvarious data responses to effectively perform resonance estimation.

FIG. 1 illustrates one example of a system for performing resonanceestimation using central nervous system, autonomic nervous system,and/or effector measures. According to various embodiments, theresonance estimation system includes a stimulus presentation device 101.In particular embodiments, the stimulus presentation device 101 ismerely a display, monitor, screen, etc., that displays stimulus materialto a user. The stimulus material may be a media clip, a commercial,pages of text, a brand image, a performance, a magazine advertisement, amovie, an audio presentation, and may even involve particular tastes,smells, textures and/or sounds. The stimuli can involve a variety ofsenses and occur with or without human supervision. Continuous anddiscrete modes are supported. According to various embodiments, thestimulus presentation device 101 also has protocol generation capabilityto allow intelligent customization of stimuli provided to multiplesubjects in different markets.

According to various embodiments, stimulus presentation device 101 couldinclude devices such as televisions, cable consoles, computers andmonitors, projection systems, display devices, speakers, tactilesurfaces, etc., for presenting the stimuli including but not limited toadvertising and entertainment from different networks, local networks,cable channels, syndicated sources, websites, internet contentaggregators, portals, service providers, etc.

According to various embodiments, the subjects are connected to datacollection devices 105. The data collection devices 105 may include avariety of neuro-response measurement mechanisms including neurologicaland neurophysiological measurements systems such as EEG, EOG, GSR, EKG,pupillary dilation, eye tracking, facial emotion encoding, and reactiontime devices, etc. According to various embodiments, neuro-response dataincludes central nervous system, autonomic nervous system, and effectordata. In particular embodiments, the data collection devices 105 includeEEG 111, EOG 113, and GSR 115. In some instances, only a single datacollection device is used. Data collection may proceed with or withouthuman supervision.

The data collection device 105 collects neuro-response data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (GSR,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In particular embodiments, datacollected is digitally sampled and stored for later analysis. Inparticular embodiments, the data collected could be analyzed inreal-time. According to particular embodiments, the digital samplingrates are adaptively chosen based on the neurophysiological andneurological data being measured.

In one particular embodiment, the resonance estimation system includesEEG 111 measurements made using scalp level electrodes, EOG 113measurements made using shielded electrodes to track eye data, GSR 115measurements performed using a differential measurement system, a facialmuscular measurement through shielded electrodes placed at specificlocations on the face, and a facial affect graphic and video analyzeradaptively derived for each individual.

In particular embodiments, the data collection devices are clocksynchronized with a stimulus presentation device 101. In particularembodiments, the data collection devices 105 also include a conditionevaluation subsystem that provides auto triggers, alerts and statusmonitoring and visualization components that continuously monitor thestatus of the subject, data being collected, and the data collectioninstruments. The condition evaluation subsystem may also present visualalerts and automatically trigger remedial actions. According to variousembodiments, the data collection devices include mechanisms for not onlymonitoring subject neuro-response to stimulus materials, but alsoinclude mechanisms for identifying and monitoring the stimulusmaterials. For example, data collection devices 105 may be synchronizedwith a set-top box to monitor channel changes. In other examples, datacollection devices 105 may be directionally synchronized to monitor whena subject is no longer paying attention to stimulus material. In stillother examples, the data collection devices 105 may receive and storestimulus material generally being viewed by the subject, whether thestimulus is a program, a commercial, printed material, or a sceneoutside a window. The data collected allows analysis of neuro-responseinformation and correlation of the information to actual stimulusmaterial and not mere subject distractions.

According to various embodiments, the resonance estimation system alsoincludes a data cleanser device 121. In particular embodiments, the datacleanser device 121 filters the collected data to remove noise,artifacts, and other irrelevant data using fixed and adaptive filtering,weighted averaging, advanced component extraction (like PCA, ICA),vector and component separation methods, etc. This device cleanses thedata by removing both exogenous noise (where the source is outside thephysiology of the subject, e.g. a phone ringing while a subject isviewing a video) and endogenous artifacts (where the source could beneurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various embodiments, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before data analyzer 181, the datacleanser device 121 like other components may have a location andfunctionality that varies based on system implementation. For example,some systems may not use any automated data cleanser device whatsoeverwhile in other systems, data cleanser devices may be integrated intoindividual data collection devices.

According to various embodiments, an optional stimulus attributesrepository 131 provides information on the stimulus material beingpresented to the multiple subjects. According to various embodiments,stimulus attributes include properties of the stimulus materials as wellas purposes, presentation attributes, report generation attributes, etc.In particular embodiments, stimulus attributes include time span,channel, rating, media, type, etc. Stimulus attributes may also includepositions of entities in various frames, object relationships, locationsof objects and duration of display. Purpose attributes includeaspiration and objects of the stimulus including excitement, memoryretention, associations, etc. Presentation attributes include audio,video, imagery, and messages needed for enhancement or avoidance. Otherattributes may or may not also be included in the stimulus attributesrepository or some other repository.

The data cleanser device 121 and the stimulus attributes repository 131pass data to the data analyzer 181. The data analyzer 181 uses a varietyof mechanisms to analyze underlying data in the system to determineresonance. According to various embodiments, the data analyzercustomizes and extracts the independent neurological andneuro-physiological parameters for each individual in each modality, andblends the estimates within a modality as well as across modalities toelicit an enhanced response to the presented stimulus material. Inparticular embodiments, the data analyzer 181 aggregates the responsemeasures across subjects in a dataset.

According to various embodiments, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,population distribution, as well as fuzzy estimates of attention,emotional engagement and memory retention responses.

According to various embodiments, the data analyzer 181 may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular embodiments, the intra-modality responsesynthesizer is configured to customize and extract the independentneurological and neurophysiological parameters for each individual ineach modality and blend the estimates within a modality analytically toelicit an enhanced response to the presented stimuli. In particularembodiments, the intra-modality response synthesizer also aggregatesdata from different subjects in a dataset.

According to various embodiments, the cross-modality responsesynthesizer or fusion device blends different intra-modality responses,including raw signals and signals output. The combination of signalsenhances the measures of effectiveness within a modality. Thecross-modality response fusion device can also aggregate data fromdifferent subjects in a dataset.

According to various embodiments, the data analyzer 181 also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular embodiments, blendedestimates are provided for each exposure of a subject to stimulusmaterials. The blended estimates are evaluated over time to assessresonance characteristics. According to various embodiments, numericalvalues are assigned to each blended estimate. The numerical values maycorrespond to the intensity of neuro-response measurements, thesignificance of peaks, the change between peaks, etc. Higher numericalvalues may correspond to higher significance in neuro-responseintensity. Lower numerical values may correspond to lower significanceor even insignificant neuro-response activity. In other examples,multiple values are assigned to each blended estimate. In still otherexamples, blended estimates of neuro-response significance aregraphically represented to show changes after repeated exposure.

According to various embodiments, the data analyzer 181 providesanalyzed and enhanced response data to a data communication device 183.It should be noted that in particular instances, a data communicationdevice 183 is not necessary. According to various embodiments, the datacommunication device 183 provides raw and/or analyzed data and insights.In particular embodiments, the data communication device 183 may includemechanisms for the compression and encryption of data for secure storageand communication.

According to various embodiments, the data communication device 183transmits data using protocols such as the File Transfer Protocol (FTP),Hypertext Transfer Protocol (HTTP) along with a variety of conventional,bus, wired network, wireless network, satellite, and proprietarycommunication protocols. The data transmitted can include the data inits entirety, excerpts of data, converted data, and/or elicited responsemeasures. According to various embodiments, the data communicationdevice is a set top box, wireless device, computer system, etc. thattransmits data obtained from a data collection device to a resonanceestimator 185. In particular embodiments, the data communication devicemay transmit data even before data cleansing or data analysis. In otherexamples, the data communication device may transmit data after datacleansing and analysis.

In particular embodiments, the data communication device 183 sends datato a resonance estimator 185. According to various embodiments, theresonance estimator 185 assesses and extracts resonance patterns. Inparticular embodiments, the resonance estimator 185 determines entitypositions in various stimulus segments and matches position informationwith eye tracking paths while correlating saccades with neuralassessments of attention, memory retention, and emotional engagement. Inparticular embodiments, the resonance estimator 185 also collects andintegrates user behavioral and survey responses with the analyzedresponse data to more effectively estimate resonance.

A variety of data can be stored for later analysis, management,manipulation, and retrieval. In particular embodiments, the repositorycould be used for tracking stimulus attributes and presentationattributes audience responses and optionally could also be used tointegrate audience measurement information.

As with a variety of the components in the system, the resonanceestimator can be co-located with the rest of the system and the user, orcould be implemented in a remote location. It could also be optionallyseparated into an assessment repository system that could be centralizedor distributed at the provider or providers of the stimulus material. Inother examples, the resonance estimator is housed at the facilities of athird party service provider accessible by stimulus material providersand/or users.

FIG. 2 illustrates examples of data models that may be provided with astimulus attributes repository. According to various embodiments, astimulus attributes data model 201 includes a channel 203, media type205, time span 207, audience 209, and demographic information 211. Astimulus purpose data model 215 may include intents 217 and objectives219. According to various embodiments, stimulus attributes data model201 also includes spatial and temporal information 221 about entitiesand emerging relationships between entities.

According to various embodiments, another stimulus attributes data model221 includes creation attributes 223, ownership attributes 225,broadcast attributes 227, and statistical, demographic and/or surveybased identifiers for automatically integrating the neuro-physiologicaland neuro-behavioral response with other attributes and meta-informationassociated with the stimulus.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with tracking and measurement of resonance.According to various embodiments, a dataset data model 301 includes anexperiment name 303 and/or identifier, client attributes 305, a subjectpool 307, logistics information 309 such as the location, date, and timeof testing, and stimulus material 311 including stimulus materialattributes.

In particular embodiments, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include shopping preferences, entertainment preferences, and financialpreferences. Shopping preferences include favorite stores, shoppingfrequency, categories shopped, favorite brands. Entertainmentpreferences include network/cable/satellite access capabilities,favorite shows, favorite genres, and favorite actors. Financialpreferences include favorite insurance companies, preferred investmentpractices, banking preferences, and favorite online financialinstruments. A variety of subject attributes may be included in asubject attributes data model 315 and data models may be preset orcustom generated to suit particular purposes.

According to various embodiments, data models for neuro-feedbackassociation 325 identify experimental protocols 327, modalities included329 such as EEG, EOG, GSR, surveys conducted, and experiment designparameters 333 such as segments and segment attributes. Other fields mayinclude experiment presentation scripts, segment length, segment detailslike stimulus material used, inter-subject variations, intra-subjectvariations, instructions, presentation order, survey questions used,etc. Other data models may include a data collection data model 337.According to various embodiments, the data collection data model 337includes recording attributes 339 such as station and locationidentifiers, the data and time of recording, and operator details. Inparticular embodiments, equipment attributes 341 include an amplifieridentifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various embodiments, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with resonance estimation. According to variousembodiments, queries are defined from general or customized scriptinglanguages and constructs, visual mechanisms, a library of presetqueries, diagnostic querying including drill-down diagnostics, andeliciting what if scenarios. According to various embodiments, subjectattributes queries 415 may be configured to obtain data from aneuro-informatics repository using a location 417 or geographicinformation, session information 421 such as testing times and dates,and demographic attributes 419. Demographics attributes includehousehold income, household size and status, education level, age ofkids, etc.

Other queries may retrieve stimulus material based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc. Experimental design basedqueries may obtain data from a neuro-informatics repository based onexperiment protocols 427, product category 429, surveys included 431,and stimulus provided 433. Other fields that may used include the numberof protocol repetitions used, combination of protocols used, and usageconfiguration of surveys.

Client and industry based queries may obtain data based on the types ofindustries included in testing, specific categories tested, clientcompanies involved, and brands being tested. Response assessment basedqueries 437 may include attention scores 439, emotion scores, 441,retention scores 443, and effectiveness scores 445. Such queries mayobtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various embodiments, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andresonance measures 507. Effectiveness assessment measures includecomposite assessment measure(s), industry/category/client specificplacement (percentile, ranking, etc.), actionable grouping assessmentsuch as removing material, modifying segments, or fine tuning specificelements, etc, and the evolution of the effectiveness profile over time.In particular embodiments, component assessment reports includecomponent assessment measures like attention, emotional engagementscores, percentile placement, ranking, etc. Component profile measuresinclude time based evolution of the component measures and profilestatistical assessments. According to various embodiments, reportsinclude the number of times material is assessed, attributes of themultiple presentations used, evolution of the response assessmentmeasures over the multiple presentations, and usage recommendations.

According to various embodiments, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various embodiments, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular embodiments,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of resonance estimation. At 601, stimulusmaterial is provided to multiple subjects in multiple geographicmarkets. According to various embodiments, stimulus includes streamingvideo and audio provided over mechanisms such as broadcast television,cable television, satellite, etc. The stimulus may be presented to usersin different geographic markets at the same or varying times. Inparticular embodiments, subjects view stimulus in their own homes ingroup or individual settings. At 603, subject responses are collectedusing a variety of modalities, such as EEG, ERP, EOG, GSR, etc. In someexamples, verbal and written responses can also be collected andcorrelated with neurological and neurophysiological responses. At 605,data is passed through a data cleanser to remove noise and artifactsthat may make data more difficult to interpret. According to variousembodiments, the data cleanser removes EEG electrical activityassociated with blinking and other endogenous/exogenous artifacts.

According to various embodiments, data analysis is performed. Dataanalysis may include intra-modality response synthesis andcross-modality response synthesis to enhance effectiveness measures. Itshould be noted that in some particular instances, one type of synthesismay be performed without performing other types of synthesis. Forexample, cross-modality response synthesis may be performed with orwithout intra-modality synthesis.

A variety of mechanisms can be used to perform data analysis. Inparticular embodiments, a stimulus attributes repository 131 is accessedto obtain attributes and characteristics of the stimulus materials,along with purposes, intents, objectives, etc. In particularembodiments, EEG response data is synthesized to provide an enhancedassessment of effectiveness. According to various embodiments, EEGmeasures electrical activity resulting from thousands of simultaneousneural processes associated with different portions of the brain. EEGdata can be classified in various bands. According to variousembodiments, brainwave frequencies include delta, theta, alpha, beta,and gamma frequency ranges. Delta waves are classified as those lessthan 4 Hz and are prominent during deep sleep. Theta waves havefrequencies between 3.5 to 7.5 Hz and are associated with memories,attention, emotions, and sensations. Theta waves are typically prominentduring states of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making. Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosurerecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular embodiments, EEGmeasurements including difficult to detect high gamma or kappa bandmeasurements are obtained, enhanced, and evaluated. Subject and taskspecific signature sub-bands in the theta, alpha, beta, gamma and kappabands are identified to provide enhanced response estimates. Accordingto various embodiments, high gamma waves (kappa-band) above 80 Hz(typically detectable with sub-cranial EEG and/ormagnetoencephalograophy) can be used in inverse model-based enhancementof the frequency responses to the stimuli.

Various embodiments of the present disclosure recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular embodiments,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular embodiments,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various embodiments,the synchronous response may be determined for multiple subjectsresiding in separate locations or for multiple subjects residing in thesame location.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious embodiments, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular embodiments, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present disclosure recognize that significancemeasures can be enhanced further using information from othermodalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousembodiments, a cross-modality synthesis mechanism performs time andphase shifting of data to allow data from different modalities to align.In some examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various embodiments, time corrected GSR measuresare used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various embodiments, ERP measures are enhancedusing EEG time-frequency measures (ERPSP) in response to thepresentation of the marketing and entertainment stimuli. Specificportions are extracted and isolated to identify ERP, DERP and ERPSPanalyses to perform. In particular embodiments, an EEG frequencyestimation of attention, emotion and memory retention (ERPSP) is used asa co-factor in enhancing the ERP, DERP and time-domain responseanalysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousembodiments, EOG and eye tracking is enhanced by measuring the presenceof lambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular embodiments, specific EEGsignatures of activity such as slow potential shifts and measures ofcoherence in time-frequency responses at the Frontal Eye Field (FEF)regions that preceded saccade-onset are measured to enhance theeffectiveness of the saccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. According to various embodiments, GSR is enhanced bycorrelating EEG/ERP responses and the GSR measurement to get an enhancedestimate of subject engagement. The GSR latency baselines are used inconstructing a time-corrected GSR response to the stimulus. Thetime-corrected GSR response is co-factored with the EEG measures toenhance GSR significance measures.

According to various embodiments, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular embodiments, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present disclosure recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

According to various embodiments, post-stimulus versus pre-stimulusdifferential measurements of ERP time domain components in multipleregions of the brain (DERP) are measured at 607. The differentialmeasures give a mechanism for eliciting responses attributable to thestimulus. For example the messaging response attributable to an ad orthe brand response attributable to multiple brands is determined usingpre-resonance and post-resonance estimates.

At 609, target versus distracter stimulus differential responses aredetermined for different regions of the brain (DERP). At 611, eventrelated time-frequency analysis of the differential response (DERPSPs)are used to assess the attention, emotion and memory retention measuresacross multiple frequency bands. According to various embodiments, themultiple frequency bands include theta, alpha, beta, gamma and highgamma or kappa. At 613, multiple trials are performed to enhanceresonance measures.

At 615, processed data is provided to a data communication device fortransmission over a network such as a wireless, wireline, satellite, orother type of communication network capable of transmitting data. Datais provided to resonance estimator at 617. According to variousembodiments, the data communication device transmits data usingprotocols such as the File Transfer Protocol (FTP), Hypertext TransferProtocol (HTTP) along with a variety of conventional, bus, wirednetwork, wireless network, satellite, and proprietary communicationprotocols. The data transmitted can include the data in its entirety,excerpts of data, converted data, and/or elicited response measures.According to various embodiments, data is sent using atelecommunications, wireless, Internet, satellite, or any othercommunication mechanisms that is capable of conveying information frommultiple subject locations for data integration and analysis. Themechanism may be integrated in a set top box, computer system, receiver,mobile device, etc.

In particular embodiments, the data communication device sends data tothe resonance estimator. According to various embodiments, the resonanceestimator combines analyzed and enhanced responses to the stimulusmaterial while using information about stimulus material attributes suchas the location, movement, acceleration, and spatial relationships ofvarious entities and objects. In particular embodiments, the resonanceestimator also collects and integrates user behavioral and surveyresponses with the analyzed and enhanced response data to moreeffectively assessment resonance patterns.

FIG. 7 illustrates an example of a technique for estimating resonance.According to various embodiments, measurements from different modalitiesare obtained at 701. According to various embodiments, measurementsincluding Differential Event Related Potential (DERP), DifferentialEvent Related Power Spectral Perturbations (DERPSPs), Pupilary Response,etc., are blended to obtain a combined measurement at 703. In particularembodiments, each measurement may have to be aligned appropriately inorder to allow blending. According to various embodiments, a resonanceestimator includes mechanisms to use and blend different measures fromacross the modalities from the data analyzer. In particular embodiments,the data includes the DERP measures, DERPSPs, pupilary response, GSR,eye movement, coherence, coupling and lambda wave based response.Measurements across modalities are blended to elicit a synthesizedmeasure of user resonance.

At 705, neuro-response measurements such as DERP, DERPSPs, pupilaryresponse, etc., are combined with statistical, demographic, and/orsurvey based information. The device contains mechanisms to integratethe neuro-physiological and neuro-behavioral response with otherattributes and meta information on the stimulus (statistical,demographic and/or survey based) for the selection of targets foradditional stimulus preparation/presentation at 713.

The resonance estimator can further include an adaptive learningcomponent that refines profiles and tracks variations responses toparticular stimuli or series of stimuli over time. This information canbe made available for other purposes, such as use of the information forpresentation attribute decision making. According to variousembodiments, the resonance estimator generates an index for use ofevaluation. Data and measurements are stored in a repository for laterretrieval and analysis.

According to various embodiments, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 8 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 8 may beused to implement a resonance measurement system.

According to particular example embodiments, a system 800 suitable forimplementing particular embodiments of the present disclosure includes aprocessor 801, a memory 803, an interface 811, and a bus 815 (e.g., aPCI bus). When acting under the control of appropriate software orfirmware, the processor 801 is responsible for such tasks such aspattern generation. Various specially configured devices can also beused in place of a processor 801 or in addition to processor 801. Thecomplete implementation can also be done in custom hardware. Theinterface 811 is typically configured to send and receive data packetsor data segments over a network. Particular examples of interfaces thedevice supports include host bus adapter (HBA) interfaces, Ethernetinterfaces, frame relay interfaces, cable interfaces, DSL interfaces,token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as data synthesis.

According to particular example embodiments, the system 800 uses memory803 to store data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present embodiments are to be consideredas illustrative and not restrictive and the disclosure is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

What is claimed is:
 1. An apparatus comprising: memory; instructions;and processor circuitry to execute the instructions to: identify firstactivity in first electroencephalography (EEG) data from a subject priorto exposure to an advertisement or entertainment; identify secondactivity in second EEG data from the subject after exposure to theadvertisement or entertainment; calculate a first event relatedpotential measurement based on the first activity; calculate a secondevent related potential measurement based on the second activity;calculate a differential event related potential measurement based onthe first event related potential measurement and the second eventrelated potential measurement; access eye tracking data obtained fromthe subject; determine a subject resonance measurement to theadvertisement or the entertainment based on the differential eventrelated potential measurement and the eye tracking data; and modify theadvertisement or entertainment in response to the subject resonancemeasurement.
 2. The apparatus of claim 1, wherein the processorcircuitry is to: calculate a first event related power spectralperturbation based on the first activity; calculate a second eventrelated power spectral perturbation based on the second activity; andcalculate a differential event related power spectral perturbation basedon the first event related power spectral perturbation and the secondevent related power spectral perturbation.
 3. The apparatus of claim 2,wherein the processor circuitry is to adjust the differential eventrelated potential measurement based on the differential event relatedpower spectral perturbation, such that the determination of the subjectresonance measurement is based on the adjusted differential eventrelated potential measurement.
 4. The apparatus of claim 1, wherein theprocessor circuitry is to: access facial emotion encoding data from thesubject; and determine the subject resonance measurement further basedon the facial emotion encoding data.
 5. The apparatus of claim 1,wherein the differential event related potential measurement is a firstdifferential event related potential measurement, and the processorcircuitry is to: identify a third activity in the first EEG data, thefirst activity associated with a first region of a brain of the subjectand the third activity associated with a second region of the brain, thesecond region different than the first region; identify a fourthactivity in the second EEG data, the second activity associated with thefirst region of the brain and the fourth activity associated with thesecond region of the brain; calculate a third event related potentialmeasurement based on the third activity; calculate a fourth eventrelated potential measurement based on the fourth activity; calculate asecond differential event related potential measurement based on thethird event related potential measurement and the fourth event relatedpotential measurement; and determine the subject resonance measurementbased on the second differential event related potential measurement. 6.The apparatus of claim 5, wherein the processor circuitry is to identifythe first activity in a first frequency band of the first EEG data andidentify the third activity in a second frequency band of the first EEGdata, the second frequency band different than the first frequency band.7. The apparatus of claim 5, wherein the processor circuitry is toperform time-based alignment of the first differential event relatedpotential measurement and the second event related potentialmeasurement.
 8. The apparatus of claim 1, wherein the differential eventrelated potential measurement is a first differential event relatedpotential measurement and the processor circuitry is to calculate atarget event related potential measurement based on targetneuro-response data and a distracter event related potential measurementbased on distracter neuro-response data to determine a seconddifferential event related potential measurement, and the processorcircuitry is to further determine the subject resonance measurement tothe advertisement or the entertainment based on the second differentialevent related potential measurement.
 9. The apparatus of claim 8,wherein the target neuro-response data is representative of exposure ofthe subject to the advertisement or the entertainment and the distracterneuro-response data is representative of exposure to material other thanthe advertisement or the entertainment.
 10. A tangible machine readablestorage device or storage disc comprising instructions that, whenexecuted, cause a machine to at least: identify first activity in firstelectroencephalography (EEG) data from a subject prior to exposure to anadvertisement or entertainment; identify second activity in second EEGdata from the subject after exposure to the advertisement orentertainment; calculate a first event related potential measurementbased on the first activity; calculate a second event related potentialmeasurement based on the second activity; calculate a differential eventrelated potential measurement based on the first event related potentialmeasurement and the second event related potential measurement; accesseye tracking data obtained from the subject; determine a subjectresonance measurement to the advertisement or the entertainment based onthe differential event related potential measurement and the eyetracking data; and modify the advertisement or entertainment in responseto the subject resonance measurement.
 11. The tangible machine readablestorage device or storage disc of claim 10, wherein the instructions,when executed, cause the machine to: calculate a first event relatedpower spectral perturbation based on the first activity; calculate asecond event related power spectral perturbation based on the secondactivity; and calculate a differential event related power spectralperturbation based on the first event related power spectralperturbation and the second event related power spectral perturbation.12. The tangible machine readable storage device or storage disc ofclaim 11, wherein the instructions, when executed, cause the machine to:adjust the differential event related potential measurement based on thedifferential event related power spectral perturbation, such that thedetermination of the subject resonance measurement is based on theadjusted differential event related potential measurement.
 13. Thetangible machine readable storage device or storage disc of claim 10,wherein the instructions, when executed, cause the machine to: accessfacial emotion encoding data from the subject; and determine the subjectresonance measurement further based on the facial emotion encoding data.14. The tangible machine readable storage device or storage disc ofclaim 10, wherein the differential event related potential measurementis a first differential event related potential measurement, and theinstructions, when executed, cause the machine to: identify a thirdactivity in the first EEG data, the first activity associated with afirst region of a brain of the subject and the third activity associatedwith a second region of the brain, the second region different than thefirst region; identify a fourth activity in the second EEG data, thesecond activity associated with the first region of the brain and thefourth activity associated with the second region of the brain;calculate a third event related potential measurement based on the thirdactivity; calculate a fourth event related potential measurement basedon the fourth activity; calculate a second differential event relatedpotential measurement based on the third event related potentialmeasurement and the fourth event related potential measurement; anddetermine the subject resonance measurement based on the seconddifferential event related potential measurement.
 15. The tangiblemachine readable storage device or storage disc of claim 14, wherein theinstructions, when executed, cause the machine to identify the firstactivity in a first frequency band of the first EEG data and identifythe third activity in a second frequency band of the first EEG data, thesecond frequency band different than the first frequency band.
 16. Thetangible machine readable storage device or storage disc of claim 14,wherein the instructions, when executed, cause the machine to performtime-based alignment of the first differential event related potentialmeasurement and the second event related potential measurement.
 17. Thetangible machine readable storage device or storage disc of claim 10,wherein the differential event related potential measurement is a firstdifferential event related potential measurement and the instructions,when executed, cause the machine to calculate a target event relatedpotential measurement based on target neuro-response data and adistracter event related potential measurement based on distracterneuro-response data to determine a second differential event relatedpotential measurement, and further determine the subject resonancemeasurement to the advertisement or the entertainment based on thesecond differential event related potential measurement.
 18. Thetangible machine readable storage device or storage disc of claim 17,wherein the target neuro-response data is representative of exposure ofthe subject to the advertisement or the entertainment and the distracterneuro-response data is representative of exposure to material other thanthe advertisement or the entertainment.
 19. An apparatus comprising:memory; instructions; and processor circuitry to execute theinstructions to: identify first activity in first electroencephalography(EEG) data from a subject prior to exposure to an advertisement orentertainment; identify second activity in second EEG data from thesubject after exposure to the advertisement or entertainment; calculatea first event related potential measurement based on the first activity;calculate a second event related potential measurement based on thesecond activity; calculate a differential event related potentialmeasurement based on the first event related potential measurement andthe second event related potential measurement; access facial emotionencoding data from the subject; determine a subject resonancemeasurement to the advertisement or the entertainment based on thedifferential event related potential measurement and the facial emotionencoding data; and modify the advertisement or entertainment in responseto the subject resonance measurement.
 20. The apparatus of claim 19,wherein the differential event related potential measurement is a firstdifferential event related potential measurement, and the processorcircuitry is to: identify a third activity in the first EEG data, thefirst activity associated with a first region of a brain of the subjectand the third activity associated with a second region of the brain, thesecond region different than the first region; identify a fourthactivity in the second EEG data, the second activity associated with thefirst region of the brain and the fourth activity associated with thesecond region of the brain; calculate a third event related potentialmeasurement based on the third activity; calculate a fourth eventrelated potential measurement based on the fourth activity; calculate asecond differential event related potential measurement based on thethird event related potential measurement and the fourth event relatedpotential measurement; and determine the subject resonance measurementbased on the second differential event related potential measurement.